@mathisimple
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Apr 13 · 10 min read · Our fraud detection neural network had 12 layers, 2.3M parameters, and 68% precision. I replaced it with Naive Bayes — 0 layers, 847 parameters, 79% precision. Training time dropped from 4 hours to 11
Join discussionApr 12 · 8 min read · Why Your PCA Pipeline Works in Notebooks But Fails in Production Our image search model's precision dropped from 82% to 61% three days after deployment. The culprit? A PCA pipeline that worked perfect
Join discussionApr 8 · 8 min read · The same dataset. The same algorithm. Opposite predictions. Not because of a bug — because one version scaled the features and the other didn't. Feature scaling is one of those topics that gets mentio
Join discussionApr 7 · 9 min read · The model isn't the problem. Nine times out of ten, when a machine learning project falls apart — bad predictions, overfitting that training metrics didn't catch, inexplicable behavior on new data — t
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